Senior Staff TLM, Data Mining and Sampling for ML and Evaluation

<div class="content-intro"><p>Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.</p></div><p><em>The Waymo Driver is performing well in more and more cities, with our scale rapidly accelerating. Our team’s job is to ensure that we can continue to evaluate safety and service quality across new cities and countries, each with their unique challenges. We need an experienced, technically hands-on engineering manager with strong quantitative skills to lead a small team and work with partners to scale eval and ML development by exploring new methods and delivering tools for mining and sampling data.In this hybrid role, you will report to a Senior Staff manager.</em></p> <p><strong>You will:</strong></p> <ul> <li>Develop tools for easy mining of novel situations that might impact safety from our driving logs for use in evaluation and ML datasets, using a mixture of ML and structured data</li> <li>Advanced sampling techniques and tuning of simulations to get strong signal for metrics and models from our datasets</li> <li>Build automation for our mining and sampling ecosystem so Waymo can rapidly scale to new cities</li> <li>Make hands-on technical contributions to both engineering and data science-oriented projects</li> <li>Provide strong management and technical leadership for a small team of data science-friendly engineers</li> <li>Partner with leads and teams across Waymo to determine and execute on strategy, including product, engineering, data science, frontend, and backend</li> </ul> <p><strong>You have:</strong></p> <ul> <li>Strong engineering design and programming skills</li> <li>Strong data fluency and analysis skills</li> <li>Familiarity with statistical methods including hypothesis testing, experiment design, and sampling</li> <li>Familiarity with foundational and contemporary ML concepts and practices</li> <li>Experience managing teams of up to 8 people</li> <li>BS/MS in Computer Science, Robotics, Statistics, Physics, Math, or other quantitative area</li> <li>7+ years of industry experience with:<br> <ul> <li>C++, SQL, and Python experience</li> <li>Navigating and modifying a large code base containing C++, SQL, and Python</li> <li>Building data processing pipelines</li> </ul> </li> </ul> <p><strong>We prefer:</strong></p> <ul> <li>Eval experience, such as building and validating metrics to measure quality in complex systems</li> <li>Strong statistical background (MSc-level stats or similar expertise from work experience)</li> <li>Strong ML background (MSc-level ML or similar expertise from work experience)</li> <li>Experience working in the AV industry and/or with simulations</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><span style="font-weight: 400;">The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. </span></p> <p><span style="font-weight: 400;">Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus p ... (truncated, view full listing at source)